Raisy vs Claude: the engine vs. the vehicle
Claude is a powerful large language model from Anthropic. Raisy is the governed operating layer that turns models like Claude into a managed AI workforce — with structure, transparency, KPIs, and memory. They are not the same category of thing, and Raisy can even run on top of Claude.
By the Raisy Team • Last updated July 28, 2026
Raisy vs Claude at a glance
| Dimension | Raisy | Claude |
|---|---|---|
| What it is | ||
| Transparency & audit | ||
| KPIs & measurement | ||
| Memory & learning | ||
| Human oversight | ||
| Setup effort | ||
| Versioning & reproducibility | ||
| Model lock-in | ||
| Pricing model | ||
| Best for |
The five differences that matter
Raisy
Raisy organizes work in a 4-level matrix: Entities (who acts) contain Processes (what they do), which are executed by Bots, which are powered by Kernels (what they know). Every layer is visualized interactively — entity hierarchies, process builders, automation flows — so you can see your entire AI workforce as a living org chart and drill into any node.
Claude
Claude gives you a conversation or an API response. If you chain fifty prompts into a business process, the structure of that system lives in your code and your head — there is no built-in visual layer for entities, processes, or agents.
Raisy
Every single LLM call inside Raisy is logged with the exact kernel versions used, the full input, the full output, and token consumption. Outputs are translated back into your business language, because a system you cannot inspect is a black box. Auditability is a design principle, not an add-on.
Claude
Claude is a black box by nature: you send a prompt, you get text. Anthropic does not expose an audit trail of your orchestration — which prompt version produced which business decision is invisible unless you build that logging yourself.
Raisy
Every level of the Raisy hierarchy carries its own KPIs, measured against previous runs, so performance is a number — not a vibe. Crucially, KPIs are computed by a deterministic code evaluator, never by the LLM itself: LLMs produce language, they do not do maths. This is how bots earn autonomy: confidence = approvals ÷ runs, tracked over time.
Claude
Claude can write a report about performance, but it cannot be the scorekeeper of its own reliability. There is no built-in concept of per-agent KPIs, approval rates, or performance trends.
Raisy
When you reject a bot's output in Raisy, that feedback triggers a kernel patch, which is quality-gated and stored as a new version. The old version is never deleted — it is the audit trail. The system provably improves with every correction while keeping a complete, inspectable history of what it knew and when.
Claude
Claude does not learn from your corrections between sessions. Each conversation starts fresh, and fine-tuning or prompt updates are manual, opaque, and easy to regress — improving the system risks losing what previously worked.
Raisy
Onboarding in Raisy starts with your company name and website. Raisy drafts a classified entity, discovers the domain, proposes processes, and assembles the bots to run them — no prompt engineering, no orchestration code. Humans stay in the loop through review, not through construction.
Claude
With the Claude API, setup means hiring engineers: designing prompts, building agent loops, wiring tools, adding logging, and maintaining it all. Powerful, but it is a software project, not an onboarding flow.
Which one is right for you?
- You have a development team and want full control over your own AI orchestration code
- You are building a single, well-defined AI feature inside your own product (chat, summarization, classification)
- You are prototyping or experimenting and want raw model access with usage-based pricing
- You specifically need Anthropic's model capabilities and are happy to build everything around them yourself
- You want AI to run business processes — sales, recruiting, fundraising, operations — not just answer prompts
- You need a complete audit trail of every AI decision, call, and version for compliance or accountability
- You want KPIs and confidence scores that quantify how every bot and process is actually performing
- You want a system that learns from every correction without ever losing its history
- You want to start from your company URL, not from an API key and an engineering roadmap
Frequently asked questions
See what a managed AI workforce looks like
Paste your company URL. Raisy builds the processes — you keep the oversight.